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检索条件"丛书名=Learning and Analytics in Intelligent Systems"

共 229 条记录，以下是81-90 订阅

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Classification of Real Time Noisy Fingerprint Images Using Flann

Classification of Real Time Noisy Fingerprint Images Using F...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Mishra, Annapurna Dehuri, Satchidananda Mallick, Pradeep Kumar Silicon Inst Technol Dept Elect & Commun Engn Silicon Hills Bhubaneswar 751024 Odisha India Fakir Mohan Univ Dept Informat & Commun Technol Balasore 756019 Odisha India KIIT Deemed Univ Sch Comp Engn Bhubaneswar 751024 Odisha India

ISBN: (纸本)9783030390334;9783030390327

In this work, we have examined a Novel Biogeography based optimized FLANN for classifying noisy fingerprints as a biometric classifier. Here the database is collected in real time from ten different persons and the features are extracted of five different classes of fingerprints using Gabor Filter bank. The results prove that this method is robust enough to classify the fingerprints with a good accuracy. Here we have used Biogeography based optimized Functional Link Artificial Neural Networks (BBO-FLANN) for the task of classification of noisy distorted fingerprint images.

关键词： Noisy Gabor FLANN

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Ensemble Classification Approach for Cancer Prognosis and Prediction

Ensemble Classification Approach for Cancer Prognosis and Pr...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Maurya, Rajesh Kumar Yadav, Sanjay Kumar Rishabh Sam Higginbottom Univ Agr Technol & Sci SHUATS Dept Comp Sci & Informat Technol Allahabad Uttar Pradesh India Maharshi Dayanand Univ Dept Comp Sci & Engn Rohtak Haryana India

ISBN: (纸本)9783030390334;9783030390327

Gene expression data mostly available as cancer data have major challenges such as analyze, pattern matching and classification. Sometime task become more complex with large number of genes and small samples are available with noise and redundant information. Meaningful correlated information from dataset is the first and most important steps to be extracted for better diagnosis through artificial intelligence (AI). Accordingly, recent work for AI based classification and prognosis are focused in two steps process that is: (a) Feature extraction, and, (b) Ensemble Classification. Feature extraction will help in eliminating redundant and irrelevant genes, whereas ensemble classifier will help to optimize the accuracy. In this paper, we use double RBF kernel function for feature selection and novel fusion-procedure for enhance the performance of three base classifiers i.e., K Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP) and Decision Tree (DT). Training of classifier is implemented based on k-fold cross validation techniques. The predicted accuracy of the proposed model has been compared with recent fusion methods such as Majority Voting, Distribution Summation and Dempster-Shafer on six benchmark cancer datasets. Experiment evaluation and result analysis gives promising and better performance than other fusion strategies, aiming at our goal-functions. Wisconsin Breast prognosis dataset is used with the proposed model for gene selection and prognosis prediction.

关键词： Gene expression Classifier fusion Kernel Feature extraction Prognosis

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Improving Co-expressed Gene Pattern Finding Using Gene Ontology

Improving Co-expressed Gene Pattern Finding Using Gene Ontol...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Baishya, R. C. Sarmah, Rosy Bhattacharyya, D. K. Tezpur Univ Tezpur Assam India

ISBN: (纸本)9783030390334;9783030390327

A semi-supervised gene co-expressed pattern finding method, PatGeneClus is presented in this paper. PatGeneClus attempts to find all possible biologically relevant gene coherent patterns from any microarray dataset by exploiting both gene expression similarity as well as GO-similarity. PatGeneClus uses a graph-based clustering algorithm called DClique to generate a set of clusters of high biological relevance. We establish the effectiveness of PatGeneClus over several benchmark datasets usingwell-known validity measures. The clusters obtained by PatGeneClus have been found to be biologically significant due to their high p-values, Q-values and clustalW scores.

关键词： Gene expression data Gene ontology Graph-based clustering Clique P value

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Some Generalized Results on Multi-criteria Decision Making Model Using Fuzzy TOPSIS Technique

Some Generalized Results on Multi-criteria Decision Making M...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Parida, P. K. CV Raman Autonomous Coll Engn Dept Math Bhubaneswar India

ISBN: (纸本)9783030390334;9783030390327

This article proposes new model involving decision making using fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) as collaborative decision-makers. FTOPSIS is very frequently used in multi-criteria decision making (MCDM). Here we give a brief introduction of FTOPSIS applied to DM. Furthermore, the concept is employed in measuring the being far off of particularized fuzzy number from one as well as other fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS). The numerical example is investigated to analyze the outcome of the alternative solution against the ideal solution.

关键词： Multi-criteria decision matrix FTOPSIS FPIS FNIS Relative closeness matrix

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Application of Machine learning to Predict Diseases Based on Symptoms in Rural India

Application of Machine Learning to Predict Diseases Based on...

引用

International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Biswal, Suvasree S. Amarnath, T. Panigrahi, Prasanta K. Biswal, Nrusingh C. Indian Inst Informat Technol Dept Informat Technol Bhubaneswar Odisha India Indian Inst Sci Educ & Res Dept Phys Sci Kolkata W Bengal India Univ Maryland Dept Radiat Oncol Sch Med Baltimore MD 21201 USA

ISBN: (纸本)9783030390334;9783030390327

Living in a place away from hospitals and medical aid, it is often difficult for some people in rural India to diagnose a disease at an early state. Though hospitals in urban areas are using advanced technology for diagnosis and prognosis, disease prediction is one of the hypercritical tasks. Providing sophisticated and accurate algorithms and techniques to overcome this issue will be revolutionary. In this paper, we present a machine learning technique using Decision Tree Algorithm (DTL) to interconnect the symptoms and rearrange them and retrieve the most probable diagnosis. This technique allows the system to self-learn without using programming. This paper presents a system for diagnosis of common diseases by entering the symptoms into the system.

关键词： Machine learning Decision Tree Algorithm Information gain Entropy Root node Disease diagnosis

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Predicting Sensitivity of Local News Articles from Odia Dailies

Predicting Sensitivity of Local News Articles from Odia Dail...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Jena, Manoj Kumar Mohanty, Sanghamitra Utkal Univ Dept Comp Sci & Applicat Bhubaneswar Odisha India

ISBN: (纸本)9783030390334;9783030390327

The news article manifests different issues of public domain. The news article are organized in different sections like local, national, international, sports, politics, editorial, readers view, sports etc. This paper purposes to categorize positive, negative and neutral local news and later on prediction of sensitivity from negative local news articles. The purpose of sensitivity analysis of negative local news articles is to set priority of action to be taken by the local administration. The sensitive news discusses the issues or event of urgent in nature, which need sudden intervention. The experiment is carried out basing on odia Syntactosemantic knowledge for categorization of 1000 local Odia news article and for sensitivity analysis tf and tf-idf score is calculated using unigram and bigram representation of data at document level, tf and tf-idf vector is passed to SVM for result and the results are analyzed by calculating the accuracy and F1 score.

关键词： SVM TF TF-Idf Syntactosemantic Sensitivity analysis News article

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Adaptive Antenna Tilt for Cellular Coverage Optimization in Suburban Scenario

Adaptive Antenna Tilt for Cellular Coverage Optimization in ...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Samal, Soumya Ranjan Dandanov, Nikolay Bandopadhaya, Shuvabrata Poulkov, Vladimir Tech Univ Sofia Fac Telecommun Sofia Bulgaria BML Munjal Univ Gurgaon India

ISBN: (纸本)9783030390334;9783030390327

Radio coverage optimization is a critical issue for mobile network operators (MNO) in the deployment of future generation cellular networks, especially on users at cell edge. The key factor that influences the coverage in mobile networks is mostly related to the configuration of the antennas and especially the angle of antenna tilt. The received signal power in a cell can be increased with proper antenna tilt, causing a significant improvement in signal-to-interference-plus-noise ratio (SINR) at the cell edge. This also leads to reduction in interference towards other cells. In this paper, a method for coverage optimization using base station antenna electrical tilt in mobile networks for suburban scenario is proposed. The main focus is on the downlink power setting by using electrical antenna tilt in the mobile network. This proposed solution uses reinforcement learning technique and the simulation results shows that the proposed algorithm can used to improve overall network performance in terms of SINR and received signal power at the cell edge with 80-100% user satisfaction. The simulation has carried out by considering a multi-cell and three sector cellular networks in a suburban scenario where the users are randomly distributed.

关键词： Self-organizing networks (SON) Coverage optimization Antenna tilt angle Reinforcement learning Cell edge users

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A Survey on FP-Tree Based Incremental Frequent Pattern Mining

A Survey on FP-Tree Based Incremental Frequent Pattern Minin...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Ahmed, Shafiul Alom Nath, Bhabesh Tezpur Univ Tepur 784028 Assam India

ISBN: (纸本)9783030390334;9783030390327

Several methods for efficient mining of frequent patterns (FP) can be found in literature. But most of the approaches assume that the whole dataset to be considered can be stored on the computers on hand main memory and the dataset is static in nature. Practically, none of the transactional datasets are static. The datasets get updated due to inclusion of new transactions or exclusion of obsolete transactions as the time advances or the user may required to generate the frequent patterns for a new threshold value for the updated database. This may generate new frequent patterns or refinement of existing patterns and it becomes practically infeasible if the process starts from scratch. Many methods have been found in literature tried to deal with the issues of incremental frequent pattern mining (FPM) but most of the algorithms are main memory dependent. Therefore in this paper, we are going to discuss some of the algorithms with their pros and cons to see whether the main memory limitation of the existing techniques can be mitigated so that it can be efficiently used in incremental scenario.

关键词： Association Rule (AR) Frequent pattern (FP) Incremental mining Frequent itemset (FI) FP-tree Rule mining (RM) Data mining (DM)

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Software Reliability Prediction with Ensemble Method and Virtual Data Point Incorporation

Software Reliability Prediction with Ensemble Method and Vir...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： Behera, Ajit Kumar Panda, Mrutyunjaya Utkal Univ Dept Comp Sci & Applicat Bhubaneswar 751004 Odisha India Silicon Inst Technol Silicon Hills Bhubaneswar 751024 India

ISBN: (纸本)9783030390334;9783030390327

Software reliability is one of the key aspects of software quality estimation and prediction during software testing period. Hence, accurate prediction of software reliability is an important but critical job. Machine learning (ML) techniques have been proven victorious in providing superior results than traditional techniques for software reliability prediction. Generally, ML models entail sufficient data for training to achieve improved generalization. Inadequate training data may lead to land at suboptimal solution. This article suggests a method of enriching training dataset through exploration and incorporation of virtual data from existing data. For boost up the overall accuracy and reducing the risk of model selection an ensemble framework of five ML methods is suggested. The extended software reliability dataset is then exposed to the constituent models as well as the ensemble approach separately to estimate the future data. Extensive simulation results on a couple of software reliability datasets reveal that our proposed model significantly improves the prediction accuracy.

关键词： Software reliability Machine learning Virtual data Interpolation Ensemble framework Artificial neural network NRMSE

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Multi-objective Biogeography-Based Optimization for Influence Maximization-Cost Minimization in Social Networks

Multi-objective Biogeography-Based Optimization for Influenc...

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International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM)

作者： De, Sagar S. Dehuri, Satchidananda Fakir Mohan Univ Dept Informat & Commun Technol Balasore 756020 Odisha India

ISBN: (纸本)9783030390334;9783030390327

The influence maximization, which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. In the past, a lot of studies were carried out to identify influential seeds from a given social graph and propagation model. Many propagation models, greedy algorithms, approximation algorithms came. However, a very less effort was made towards influence maximization-cost minimization problem. Therefore in this work, we have suggested a multi-objective biography-based optimization strategy to maximize influence while minimizing the cost. The strategy combines the best attributes of biogeography-based optimization and non-dominated sorting genetic algorithm II. A multi-objective ranking and selection strategy improve the convergence rate. Our empirical analysis on many real-life networks confers the effectiveness of the algorithm in terms of both influence spread and time efficiency.

关键词： Information diffusion Influence maximization Cost minimization Multi-objective optimization Biogeography-based optimization

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